The wooden artifacts from Schöningen's Spear Horizon and their place in human evolution.

Ethnographic records show that wooden tools played a pivotal role in the daily lives of hunter-gatherers including food procurement tools used in hunting (e.g., spears, throwing sticks) and gathering (e.g. digging sticks, bark peelers), as well as, domestic tools (e.g., handles, vessels). However, wood rarely survives in the archeological record, especially in Pleistocene contexts and knowledge of prehistoric hunter-gatherer lifeways is strongly biased by the survivorship of more resilient materials such as lithics and bones. Consequently, very few Paleolithic sites have produced wooden artifacts and among them, the site of Schöningen stands out due to its number and variety of wooden tools. The recovery of complete wooden spears and throwing sticks at this 300,000-y-old site (MIS 9) led to a paradigm shift in the hunter vs. scavenger debate. For the first time and almost 30 y after their discovery, this study introduces the complete wooden assemblage from Schöningen 13 II-4 known as the Spear Horizon. In total, 187 wooden artifacts could be identified from the Spear Horizon demonstrating a broad spectrum of wood-working techniques, including the splitting technique. A minimum of 20 hunting weapons is now recognized and two newly identified artifact types comprise 35 tools made on split woods, which were likely used in domestic activities. Schöningen 13 II-4 represents the largest Pleistocene wooden artifact assemblage worldwide and demonstrates the key role woodworking had in human evolution. Finally, our results considerably change the interpretation of the Pleistocene lakeshore site of Schöningen.

[Features of detection and interpretation of intravital and postmortem changes according to the results of traditional X-ray and X-ray computed tomography of objects from historical graves and artefacts]. / Osobennosti vyyavleniya i interpretatsii prizhiznennykh i posmertnykh izmenenii po rezul'tatam traditsionnoi rentgenografii i rentgenovskoi komp'yuternoi tomografii ob"ektov iz istoricheskikh zakhoronenii i artefaktov.

OBJECTIVE: To study emergence mechanism, physical nature, pattern of intravital and postmortem changes of biological and non-biological objects originated in the period from 1550 to 1918 yr. using traditional X-ray and X-ray computed tomography. MATERIAL AND METHODS: The relics of Saint Macarius the Roman of Novgorod, the remains of the First Reverend of the Resurrection Novodevichy Convent in Saint Petersburg Mother Superior Theophania, damages on the chair leg on which Tsesarevich Alexey sat during the shooting of Russian Emperor Nicholas II, his family and entourage in 1918 in Yekaterinburg were stidued. RESULTS AND CONCLUSION: The application of highly informative methods of traditional X-ray and X-ray computed tomography of biological and non-biological objects showed their high informativity and allowed to correctly interpret the emergence mechanism, physical nature, pattern of intravital and postmortem changes of skeleton bones and historical artefact (chair legs) originated long ago. The necessity of special professional training and advanced training of experts in forensic radiology to prevent possible diagnostic and expert errors has been substantiated.

EEG complexity measures for detecting mind wandering during video-based learning.

This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system's regularity like multiscale permutation entropy (MPE), and metrics that reflect a system's dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

Rare wooden artifacts show the smarts of early Neanderthals.

Complex tools from 300,000-year-old deposit at Schöningen in Germany point to a "wood age".

Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging.

OBJECTIVE: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS: 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS: The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

High-resolution free-breathing hepatobiliary phase MRI of the liver using XD-GRASP.

PURPOSE: To evaluate the performance of high-resolution free-breathing (FB) hepatobiliary phase imaging of the liver using the eXtra-Dimension Golden-angle RAdial Sparse Parallel (XD-GRASP) MRI technique. METHODS: Fifty-eight clinical patients (41 males, mean age = 52.9 ± 12.9) with liver lesions who underwent dynamic contrast-enhanced MRI with a liver-specific contrast agent were prospectively recruited for this study. Both breath-hold volumetric interpolated examination (BH-VIBE) imaging and FB imaging were performed during the hepatobiliary phase. FB images were acquired using a stack-of-stars golden-angle radial sequence and were reconstructed using the XD-GRASP method. Two experienced radiologists blinded to acquisition schemes independently scored the overall image quality, liver edge sharpness, hepatic vessel clarity, conspicuity of lesion, and overall artifact level of each image. The non-parametric paired two-tailed Wilcoxon signed-rank test was used for statistical analysis. RESULTS: Compared to BH-VIBE images, XD-GRASP images received significantly higher scores (P < 0.05) for the liver edge sharpness (4.83 ± 0.45 vs 4.29 ± 0.46), the hepatic vessel clarity (4.64 ± 0.67 vs 4.15 ± 0.56) and the conspicuity of lesion (4.75 ± 0.53 vs 4.31 ± 0.50). There were no significant differences (P > 0.05) between BH-VIBE and XD-GRASP images for the overall image quality (4.61 ± 0.50 vs 4.74 ± 0.47) and the overall artifact level (4.13 ± 0.44 vs 4.05 ± 0.61). CONCLUSION: Compared to conventional BH-VIBE MRI, FB radial acquisition combined with XD-GRASP reconstruction facilitates higher spatial resolution imaging of the liver during the hepatobiliary phase. This enhancement can significantly improve the visualization and evaluation of the liver.

DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction.

Magnetic resonance imaging (MRI) is non-invasive and crucial for clinical diagnosis, but it has long acquisition time and aliasing artifacts. Accelerated imaging techniques can effectively reduce the scanning time of MRI, thereby decreasing the anxiety and discomfort of patients. Vision Transformer (ViT) based methods have greatly improved MRI image reconstruction, but their computational complexity and memory requirements for the self-attention mechanism grow quadratically with image resolution, which limits their use for high resolution images. In addition, the current generative adversarial networks in MRI reconstruction are difficult to train stably. To address these problems, we propose a Local Vision Transformer (LVT) based adversarial Diffusion model (Diff-GAN) for accelerating MRI reconstruction. We employ a generative adversarial network (GAN) as the reverse diffusion model to enable large diffusion steps. In the forward diffusion module, we use a diffusion process to generate Gaussian mixture distribution noise, which mitigates the gradient vanishing issue in GAN training. This network leverages the LVT module with the local self-attention, which can capture high-quality local features and detailed information. We evaluate our method on four datasets: IXI, MICCAI 2013, MRNet and FastMRI, and demonstrate that Diff-GAN can outperform several state-of-the-art GAN-based methods for MRI reconstruction.

Fast and accessible T2 mapping using off-resonance corrected DESPOT2 with application to 3D prostate.

PURPOSE: Most T1 and T2 mapping take long acquisitions or needs specialized sequences not widely accessible on clinical scanners. An available solution is DESPOT1/T2 (Driven equilibrium single pulse observation of T1/T2). DESPOT1/T2 uses Spoiled gradient-echo (SPGR) and balanced Steady-State Free Precession (bSSFP) sequences, offering an accessible and reliable way for 3D accelerated T1/T2 mapping. However, bSSFP is prone to off-resonance artifacts, limiting the application of DESPOT2 in regions with high susceptibility contrasts, like the prostate. Our proposal, DESPO+, employs the full bSSFP and SPGR models with a dictionary-based method to reconstruct 3D T1/T2 maps in the prostate region without off-resonance banding. METHODS: DESPO+ modifies the bSSFP acquisition of the original variable flip angle DESPOT2. DESPO+ uses variable repetition and echo times, employing a dictionary-based method of the full bSSFP and SPGR models to reconstruct T1, T2, and Proton Density (PD) simultaneously. The proposed DESPO+ method underwent testing through simulations, T1/T2 phantoms, and on fourteen healthy subjects. RESULTS: The results reveal a significant reduction in T2 map banding artifacts compared to the original DESPOT2 method. DESPO+ approach reduced T2 errors by up to seven times compared to DESPOT2 in simulations and phantom experiments. We also synthesized in-vivo T1-weighted/T2-weighted images from the acquired maps using a spin-echo model to verify the map's quality when lacking a reference. For in-vivo imaging, the synthesized images closely resemble those from the clinical MRI protocol, reducing scan time by around 50% compared to traditional spin-echo T1-weighted/T2-weighted acquisitions. CONCLUSION: DESPO+ provides an off-resonance insensitive and clinically available solution, enabling high-resolution 3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire prostate, all achieved within a short scan time of 3.6 min, similar to DESPOT1/T2.

Minimal logical teleology in artifacts and biology connects the two domains and frames mechanisms via epistemic circularity.

The understanding of artifacts and biological phenomena has often influenced each other. This work argues that at the core of these epistemic bridges there are shared teleological notions and explanations manifested in analogies between artifacts and biological phenomena. To this end, I first propose a focus on the logical structure of minimal teleological explanations, which renders said epistemic bridges more evident than an ontological or metaphysical approach to teleology, and which can be used to describe scientific practices in different areas by virtue of formal generality and minimalism (section 2). Second, I show how this approach highlights some epistemic features shared by the understanding of artifacts and biological phenomena, like a specific kind of epistemic circularity, and how functional analogies between artifacts and biological phenomena translate such epistemic circularity from one domain to the other (section 3). Third, I conduct a case study on the scientific practice around the brain's "compass", showing how the understanding of artifacts influences purpose ascription and measurement, and frames mechanisms in biology, especially in areas where purpose ascription is most difficult, like cognitive neuroscience (sections 4 and 5).

Fully bioresorbable hybrid opto-electronic neural implant system for simultaneous electrophysiological recording and optogenetic stimulation.

Bioresorbable neural implants based on emerging classes of biodegradable materials offer a promising solution to the challenges of secondary surgeries for removal of implanted devices required for existing neural implants. In this study, we introduce a fully bioresorbable flexible hybrid opto-electronic system for simultaneous electrophysiological recording and optogenetic stimulation. The flexible and soft device, composed of biodegradable materials, has a direct optical and electrical interface with the curved cerebral cortex surface while exhibiting excellent biocompatibility. Optimized to minimize light transmission losses and photoelectric artifact interference, the device was chronically implanted in the brain of transgenic mice and performed to photo-stimulate the somatosensory area while recording local field potentials. Thus, the presented hybrid neural implant system, comprising biodegradable materials, promises to provide monitoring and therapy modalities for versatile applications in biomedicine.

Thin-slice reverse encoding distortion correction DWI facilitates visualization of non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma and surrounding normal structures.

BACKGROUND: To evaluate the clinical usefulness of thin-slice echo-planar imaging (EPI)-based diffusion-weighted imaging (DWI) with an on-console distortion correction technique, termed reverse encoding distortion correction DWI (RDC-DWI), in patients with non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma. METHODS: Patients with non-functioning PitNET/pituitary adenoma who underwent 3-T RDC-DWI between December 2021 and September 2022 were retrospectively enrolled. Image quality was compared among RDC-DWI, DWI with correction for distortion induced by B0 inhomogeneity alone (B0-corrected-DWI), and original EPI-based DWI with anterior-posterior phase-encoding direction (AP-DWI). Susceptibility artifact, anatomical visualization of cranial nerves, overall tumor visualization, and visualization of cavernous sinus invasion were assessed qualitatively. Quantitative assessment of geometric distortion was performed by evaluation of anterior and posterior displacement between each DWI and the corresponding three-dimensional T2-weighted imaging. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient values were measured. RESULTS: Sixty-four patients (age 70.8 ± 9.9 years [mean ± standard deviation]; 33 females) with non-functioning PitNET/pituitary adenoma were evaluated. In terms of susceptibility artifacts in the frontal and temporal lobes, visualization of left trigeminal nerve, overall tumor visualization, and anterior displacement, RDC-DWI performed the best and B0-corrected-DWI performed better than AP-DWI. The right oculomotor and right trigeminal nerves were better visualized by RDC-DWI than by B0-corrected-DWI and AP-DWI. Visualization of cavernous sinus invasion and posterior displacement were better by RDC-DWI and B0-corrected-DWI than by AP-DWI. SNR and CNR were the highest for RDC-DWI. CONCLUSIONS: RDC-DWI achieved excellent image quality regarding susceptibility artifact, geometric distortion, and tumor visualization in patients with non-functioning PitNET/pituitary adenoma. RELEVANCE STATEMENT: RDC-DWI facilitates excellent visualization of the pituitary region and surrounding normal structures, and its on-console distortion correction technique is convenient. RDC-DWI can clearly depict cavernous sinus invasion of PitNET/pituitary adenoma even without contrast medium. KEY POINTS: • RDC-DWI is an EPI-based DWI technique with a novel on-console distortion correction technique. • RDC-DWI corrects distortion due to B0 field inhomogeneity and eddy current. • We evaluated the usefulness of thin-slice RDC-DWI in non-functioning PitNET/pituitary adenoma. • RDC-DWI exhibited excellent visualization in the pituitary region and surrounding structures. • In addition, the on-console distortion correction of RDC-DWI is clinically convenient.

Riboformer: a deep learning framework for predicting context-dependent translation dynamics.

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Remote photoplethysmography based on reflected light angle estimation.

Objective. In previous studies, the factors affecting the accuracy of imaging photoplethysmography (iPPG) heart rate (HR) measurement have been focused on the light intensity, facial reflection angle, and motion artifacts. However, the factor of specularly reflected light has not been studied in detail. We explored the effect of specularly reflected light on the accuracy of HR estimation and proposed an estimation method for the direction of specularly radiated light.Approach. To study the HR measurement accuracy influenced by specularly reflected light, we control the component of specularly reflected light by controlling its angle. A total of 100 videos from four different reflected light angles were collected, and 25 subjects participated in the dataset collection. We extracted angles and illuminations for 71 facial regions, fitting sample points through interpolation, and selecting the angle corresponding to the maximum weight in the fitted curve as the estimated reflected angle.Main results. The experimental results show that higher specularly reflected light compromises HR estimation accuracy under the same value of light intensity. Notably, at a 60° angle, the HR accuracy (ACC) increased by 0.7%, while the signal-to-noise ratio and Pearson correlation coefficient increased by 0.8 dB and 0.035, respectively, compared to 0°. The overall root mean squared error, standard deviation, and mean error of our proposed reflected light angle estimation method on the illumination multi-angle incidence (IMAI) dataset are 1.173°, 0.978°, and 0.773°. The average Pearson value is 0.8 in the PURE rotation dataset. In addition, the average ACC of HR measurements in the PURE dataset is improved by 1.73% in our method compared to the state-of-the-art traditional methods.Significance. Our method has great potential for clinical applications, especially in bright light environments such as during surgery, to improve accuracy and monitor blood volume changes in blood vessels.

Digital registration versus cone-beam computed tomography for evaluating implant position: a prospective cohort study.

BACKGROUND: Postoperative cone-beam computed tomography (CBCT) examination is considered a reliable method for clinicians to assess the positions of implants. Nevertheless, CBCT has drawbacks involving radiation exposure and high costs. Moreover, the image quality can be affected by artifacts. Recently, some literature has mentioned a digital registration method (DRM) as an alternative to CBCT for evaluating implant positions. The aim of this clinical study was to verify the accuracy of the DRM compared to CBCT scans in postoperative implant positioning. MATERIALS AND METHODS: A total of 36 patients who received anterior maxillary implants were included in this clinical study, involving a total of 48 implants. The study included 24 patients in the single implant group and 12 patients in the dual implant group. The postoperative three-dimensional (3D) positions of implants were obtained using both CBCT and DRM. The DRM included three main steps. Firstly, the postoperative 3D data of the dentition and intraoral scan body (ISB) was obtained through the intraoral scan (IOS). Secondly, a virtual model named registration unit which comprised an implant replica and a matching ISB was created with the help of a lab scanner and reverse engineering software. Thirdly, by superimposing the registration unit and IOS data, the postoperative position of the implant was determined. The accuracy of DRM was evaluated by calculating the Root Mean Square (RMS) values after superimposing the implant positions obtained from DRM with those from postoperative CBCT. The accuracy of DRM was compared between the single implant group and the dual implant group using independent sample t-tests. The superimposition deviations of CBCT and IOS were also evaluated. RESULTS: The overall mean RMS was 0.29 ± 0.05 mm. The mean RMS was 0.30 ± 0.03 mm in the single implant group and 0.29 ± 0.06 mm in the dual implant group, with no significant difference (p = 0.27). The overall registration accuracy of the IOS and CBCT data ranged from 0.14 ± 0.05 mm to 0.21 ± 0.08 mm. CONCLUSION: In comparison with the 3D implant positions obtained by CBCT, the implant positions located by the DRM showed clinically acceptable deviation ranges. This method can be used in single and dual implant treatments to assess the implant positions.

Design, Fabrication and Evaluation of a Stretchable High-Density Electromyography Array.

The adoption of high-density electrode systems for human-machine interfaces in real-life applications has been impeded by practical and technical challenges, including noise interference, motion artefacts and the lack of compact electrode interfaces. To overcome some of these challenges, we introduce a wearable and stretchable electromyography (EMG) array, and present its design, fabrication methodology, characterisation, and comprehensive evaluation. Our proposed solution comprises dry-electrodes on flexible printed circuit board (PCB) substrates, eliminating the need for time-consuming skin preparation. The proposed fabrication method allows the manufacturing of stretchable sleeves, with consistent and standardised coverage across subjects. We thoroughly tested our developed prototype, evaluating its potential for application in both research and real-world environments. The results of our study showed that the developed stretchable array matches or outperforms traditional EMG grids and holds promise in furthering the real-world translation of high-density EMG for human-machine interfaces.

3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction.

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed tomography (CT) imaging. We introduce a method that can reduce both artifacts simultaneously. To validate the proposed method, we carried out CT scan experiments using plaster dental casts created from dental impressions. After the artifact correction, the CT image quality was greatly improved in terms of image uniformity, contrast-to-noise ratio (CNR), and edge sharpness. We examined the correction effects on the accuracy of the 3D models generated from the CT images. As referenced to the 3D models derived from the optical scan data, the root mean square (RMS) errors were reduced by 8.8~71.7% for three dental casts of different sizes and shapes. Our method offers a solution to challenges posed by artifacts in CT scanning of plaster dental casts, leading to enhanced 3D model accuracy. This advancement holds promise for dental professionals seeking precise digital modeling for diverse applications in dentistry.

MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion.

Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.